THE INFLUENCE OF UNCERTAINTY VARIABLES ON PROJECT COST ESTIMATION

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THE INFLUENCE OF UNCERTAINTY VARIABLES ON PROJECT COST ESTIMATION

ANITA RAUZANA

UNIVERSITI SAINS MALAYSIA

2015

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THE INFLUENCE OF UNCERTAINTY VARIABLES ON PROJECT COST ESTIMATION

by

ANITA RAUZANA

Thesis Submitted in fulfillment of requirements for the degree of

Doctor of Philosophy

January 2015

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ACKNOWLEDGEMENTS

First and foremost, I would like to thank Allah SWT for the mercy to complete this research and because of his protection throughout the whole duration of my study.

This thesis would not have been completed without the contributions, guidance, support and sacrifices of several individuals. First, I would like to thank my main supervisor Professor Abu Hassan Bin Abu Bakar. He is the source of motivation behind the success of this research. His vast and long experience in project management, especially in quantitative studies has helped me a lot in pursuing my higher studies and in broadening my knowledge. His encouraging words motivated me to go further in my study confidently. Second, my gratitude also goes to Dr. Mohd Hanizun Hanafi, my co-supervisor. His ideas and suggestions helped me a lot while I was facing some difficulties in conducting my study. Third, my gratitude also goes to the lectures in School of Housing Building and Planning, University Sains Malaysia (USM).

Last but not least, my deepest gratitude goes to my parents who have always supported me. They have given me their greatest support, not only during my study, but also in my daily life. I would like to thank my husband, Wira Dharma for his dedication, support and understanding throughout my study in USM. Not forgetting my children, Muhammad Hafidz Akbar, Najwa Ranita Lovena, and Mecca Dhanita Lovena, for not loving me less despite my constant absence from their lives during this study.

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TABLE OF CONTENTS

Acknowledgements ii

Table of Contents iii

List of Tables viii

List of Figures ix

List of Abbreviations xi

Abstrak xii

Abstract xiv

CHAPTER 1: INTRODUCTION

1.1 Background of the Study 1

1.2 Research Problem 4

1.3 Research Gap 5

1.4 Research Questions (RQs) 7

1.5 Research Objectives (ROs) 8

1.6 Significance of the Research 8

1.7 Scope of the Research 10

1.8 Definition of Key Terms 11

1.9 Organisation of the Thesis 13

CHAPTER 2: LITERATURE REVIEW

2.1 Introduction 15

2.2 Estimated Costs 15

2.2.1 Direct Costs 19

2.2.2 Indirect Costs 21

2.2.2.1 Overhead Costs 21

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2.2.2.2 Contingencies 22

2.2.2.3 Profitability (Profit) 24

2.3 Uncertainty Variables in Construction Projects 24

2.3.1 External Uncertainty Factors 25

2.3.1.1 Economic Variables 25

2.3.1.2 Social or Cultural Variables 32

2.3.1.3 Geography Variables 36

2.3.1.4 Government Variables 41

2.3.2 Factors of Internal Uncertainty 46

2.3.2.1 Construction of the Project Complexity Variables 46 2.3.2.2 Project Management Handling Variables 53

2.4 Research Framework 61

2.5 Summary 63

CHAPTER 3: RESEARCH METHODOLOGY

3.1 Introduction 64

3.2 Identification of Uncertainty Variables on Construction Projects 64

3.3 Data Collection 66

3.4 Questionnaire Design 67

3.5 Sampling 67

3.5.1 Study Location 71

3.5.2 General Economics of Indonesia 71

3.5.3 General Construction Sector 74

3.5.4 Study Location in Medan 77

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Page 3.6 Data Analysis and Interpretation of Questionnaire Results 82

3.6.1 Pilot Survey 82

3.6.2 Validity Test 83

3.6.3 Validation of the Qestionnaire 84

3.6.4 Reliability Test 88

3.6.5 Descriptive Statistics 91

3.6.6 Regression Analysis 92

3.6.7 Ordinal Regression Analysis 93

3.7 Hypothesis Test 97

3.8 Confirmation Interview 98

3.9 Summary 101

CHAPTER 4: ANALYSIS AND FINDINGS

4.1 Introduction 102

4.2 The First Objective of the Research 102

4.3 The Second Objective of the Research 103

4.3.1 Economic Variables 103

4.3.2 Social or Cultural Variables 106

4.3.3 Geographic Variables 108

4.3.4 Government Variables 111

4.3.5 Complexity of Project Variables 114

4.3.6 Project Management Handling 117

4.3.7 Contingency Cost Percentage Variable 120

4.4 The Third Objective of the Research 121

4.4.1 Ordinal Regression Analysis 121

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4.4.2 Analysis Results 125

4.5 Summary 129

CHAPTER 5: FINDINGS AND DISCUSSION

5.1 Introductiom 130

5.2 The First Objective of the Research 130

5.3 The Second Objectives of the Research 130

5.3.1 Economic Variables 131

5.3.2 Social or Cultural Variables 136

5.3.3 Geography Variables 141

5.3.4 Government Variables 145

5.3.5 Complexity of Project Variables 151

5.3.6 Project Management Handling 159

5.3.7 Contingency Cost 165

5.4 The Third Objective of the Research 166

5.5 Interpretation of Data Analysis 180

CHAPTER 6: CONCLUSIONS AND RECOMMENDATIONS

6.1 Introductiom 182

6.2 Conclusions of the Research 182

6.2.1 The First Objective of Research 182 6.2.2 The Second Objective of Research 183 6.2.3 The Third Objective of Research 183

6.3 Contributions to Knowledge 185

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6.4 Contribution to Practice 186

6.5 Limitations of the study 187

6.6 Recommendations for Future Research 187

REFERENCES 189

APPENDICES

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LIST OF TABLES

Page Table 1.1 Qualification of Construction Services Business 11

Table 1.2 Definitions of The Terms 11

Table 2.1 The Uncertainties Variable That Influence Project Cost

Estimate Based on Other Studies 59

Table 3.1 Variables of Uncertainty in Estimating the Cost of

Construction Project Offers 65

Table 3.2 Information of Respondents 67

Table 3.3 Growth Rate of Gross Domestic Product by Industrial

Origin (Percent) 75

Table 3.4 The New Uncertainty Variables 86

Table 3.5 Uncertainty Variables Based on References 86 Table 3.6 Variables Uncertainty in Estimating the Cost of Construction

Project Offers 87

Table 3.7 Cronbach Alpha Coefficients 89

Table 3.8 Cronbach Alpha Coefficients for Uncertainty Variables 90 Table 4.1 Descriptive Statistics: Economic Variables 103 Table 4.2 Descriptive Statistics: Social or Cultural Variables 106 Table 4.3 Descriptive Statistics: Geography Variables 109 Table 4.4 Descriptive Statistics: Government Variables 111 Table 4.5 Descriptive Statistics: Complexity of Project 114 Table 4.6 Descriptive Statistics: Project Management Handling 118 Tablle 4.7 Descriptive Statistics: Contingency Cost 120

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Table 4.8 Model Fitting Information 121

Table 4.9 Goodness-of-Fit 122

Table 4.10 Test of Parallel Lines 123

Table 4.11 Pseudo R-Square 123

Table 4.12 Parameter Estimates 124

Table 4.13 Confirmation Interview Result 128

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LIST OF FIGURES

Page

Figure 2.1 Research framework 62

Figure 3.1 Map of Indonesia 72

Figure 3.2 GDP growth rate Quarter I-2011 to Quarter II-2012 (percent) 73 Figure 3.3 Development of economic growth in North Sumatra 78 Figure 3.4 Construction products sales by retail trade survey northern

Sumatra 79

Figure 3.5 Map of the island of Sumatra 80

Figure 3.6 Map of North Sumatra 81

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LIST OF ABBREVIATIONS

AACE Association for the Advancement of Cost Engineering BTN Bank Tabungan Negara, Indonesia

GDP Gross domestic product

GRDP Gross regional domestic product UMR Regional minimum wage WCC Working capital credit

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PENGARUH PEMBOLEH UBAH KETIDAKPASTIAN KE ATAS PENGANGGARAN KOS PROJEK

ABSTRAK

Anggaran kos ialah satu ramalan tentang kuantiti, kos, dan harga sumber yang diperlukan oleh skop sesuatu opsyen pelaburan aset, aktiviti, atau projek.

Dalam peramalan kos sesuatu projek, kos yang akan dibelanjakan pada masa pembinaan projek tersebut tidak boleh diketahui dengan pasti. Kos projek berkait rapat dengan pemboleh ubah yang tidak boleh dianggar dengan pasti, ataupun pemboleh ketidakpastian semasa fasa pembinaan. Dalam jangkaan kerugian yang ditanggung sebagai akibat pemboleh ubah yang tidak boleh dianggar dengan pasti pada masa anggaran dibuat, kos tak langsung yang dicaskan kepada kos risiko terpaksa diperuntukkan. Kos ini dikenali sebagai kos kontingensi.

Kajian ini bertujuan untuk mengenal pasti pemboleh-pemboleh ubah ketidakpastian dan menentukan tahap pengaruh pemboleh-pemboleh ubah ini dalam penganggaran kos projek. Tambahan lagi, kajian ini bertujuan untuk mengenal pasti pemboleh-pemboleh ubah ketidakpastian yang penting yang mempunyai pengaruh yang lebih besar dalam penganggaran kos projek, untuk mengkaji kesan pemboleh- pemboleh ubah penting ini ke atas kos kontingensi.

Data yang digunakan dalam penyelidikan ini termasuk data primer dan data sekunder. Pengumpulan data telah dilaksanakan di kota Medan. Responden sasaran kajian ini terdiri daripada 151 penganggar pakar dalam syarikat kontrak. Data dalam kajian ini dianalisis menggunakan statistik perihalan dan kaedah regresi ordinal.

Kajian ini menggunakan soal selidik dan temu bual untuk mengumpul data tentang

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pemboleh-pemboleh ubah ketidakpastian yang paling berpengaruh dalam penganggaran kos projek, berasaskan persepsi dan pendapat responden. Bagi pemboleh ubah kadar inflasi, daripada 6 pemboleh ubah bebas, hanya 3 pemboleh ubah mempunyai pengaruh ke atas kos kontingensi. Pemboleh-pemboleh ubah tersebut ialah geografi (0.003), dasar kerajaan (0,001), kendalian dan pengurusan (0.000). Pemboleh-pemboleh ubah ini signifikan pada tahap keertian 0.05. Jumlah kos kontingensi yang dikenal pasti bagi menghadapi ketidakpastian adalah 11% ≤ C

≤ 15% daripada kos pembinaan keseluruhannya.

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THE INFLUENCE OF UNCERTAINTY VARIABLES ON PROJECT COST ESTIMATION

ABSTRACT

Cost estimate is a prediction of quantities, cost, and price of resources required by the scope of an asset investment option, activity, or project. In estimating the cost of a project, the cost to be incurred at the time of construction of the project cannot be known for certain. Project cost is closely related to variables that cannot be estimated with certainty or uncertainty variables during the construction phase. In anticipation of losses that would be incurred as a result of variables that cannot be estimated with certainty at the time of estimation, indirect costs charged to the cost of risk have to be allocated. This cost is known as contingency cost.

This study aims to identify these uncertainty variables and to determine their level of influence in the estimation of project cost. Furthermore, this study aims to identify the key uncertainty variables that have greater influence in the estimation of project cost, to study the impact of the key uncertainty variables on contingency cost.

Data used in this research include primary data and secondary data. Data collection was conducted in the city of Medan. Target respondents in this study are 151 expert estimators in contraction companies. Data in this study were analysed using descriptive statistics and ordinal regression method. The study used the questionnaire and interviews to gather data on the uncertainty variables most influential in the estimation of project cost, based on the perceptions or opinions of respondents. For the inflation rate variable, from 6 independent variables, only 3 variables have influence the contingency cost. They are geography (0.003), government policy (0.001) handling and management (0.000). These variables were

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significant at 0.05 significance level. The amount of contingency cost that was identified for facing uncertainty was 11% ≤ C ≤ 15% of the overall construction costs.

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APPENDIX 8

Descriptives

DESCRIPTIVES VARIABLES=E1 E2 E3 E4 E5 /STATISTICS=MEAN STDDEV MIN MAX.

[DataSet0]

Table Descriptive Statistics

N Minimum Maximum Mean Std. Deviation

E1 151 3 5 4.46 .764

E2 151 1 5 3.52 1.051

E3 151 3 5 3.90 .700

E4 151 1 4 3.52 .609

E5 151 3 5 3.68 .734

Valid N (listwise) 151

DESCRIPTIVES VARIABLES=S1 S2 S3 S4 S5 /STATISTICS=MEAN STDDEV MIN MAX.

[DataSet0]

Descriptive Statistics

S1 151 1 5 3.89 .801

S2 151 1 5 3.87 .899

S3 151 3 5 3.92 .560

S4 151 1 4 3.75 .741

S5 151 3 5 3.78 .692

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DESCRIPTIVES VARIABLES=G1 G2 G3 G4 G5 /STATISTICS=MEAN STDDEV MIN MAX.

[DataSet0]

G1 151 1 4 3.42 .724

G2 151 3 5 3.92 .627

G3 151 2 4 3.61 .600

G4 151 2 4 3.48 .575

G5 151 3 5 3.68 .570

DESCRIPTIVES VARIABLES=K1 K2 K3 K4 K5 K6 /STATISTICS=MEAN STDDEV MIN MAX.

[DataSet0]

K1 151 3 5 3.75 .675

K2 151 2 4 3.49 .610

K3 151 2 4 3.31 .556

K4 151 3 5 3.71 .639

K5 151 3 5 4.07 .660

K6 151 3 5 3.73 .621

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DESCRIPTIVES VARIABLES=P1 P2 P3 P4 P5 P6 P7 /STATISTICS=MEAN STDDEV MIN MAX.

[DataSet0]

P1 151 3 5 3.82 .740

P2 151 3 5 3.72 .518

P3 151 3 5 3.74 .605

P4 151 3 5 3.72 .634

P5 151 3 5 3.81 .608

P6 151 2 5 3.62 .597

P7 151 1 3 1.16 .402

DESCRIPTIVES VARIABLES=M1 M2 M3 M4 M5 /STATISTICS=MEAN STDDEV MIN MAX.

M1 151 3 5 3.67 .630

M2 151 3 5 3.57 .638

M3 151 3 5 3.51 .682

M4 151 3 5 3.65 .704

M5 151 3 5 3.93 .709

[DataSet0]

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DESCRIPTIVES VARIABLES=Y

/STATISTICS=MEAN STDDEV MIN MAX.

[DataSet0]

Y 151 1 4 2.76 .822

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APPENDIX 9

PLUM Y WITH TOT_E TOT_SOC TOT_GE TOT_GO TOT_CP TOT_M

/CRITERIA=CIN(95) DELTA(0) LCONVERGE(0) MXITER(100) MXSTEP(5) PCONVERGE(1.0E-6) SINGULAR(1.0E-8)

/LINK=LOGIT

/PRINT=FIT PARAMETER SUMMARY TPARALLEL.

PLUM - Ordinal Regression

[DataSet1] C:\Users\Public\Documents\Anitahbp.sav

Case Processing Summary

N Marginal

Percentage

Y

1 8 5.3%

2 49 32.5%

3 65 43.0%

4 29 19.2%

Valid 151 100.0%

Missing 0

Total 151

Model Fitting Information

Model -2 Log

Likelihood

Chi-Square df Sig.

Intercept Only 357.030

Final 291.140 65.890 6 .000

Link function: Logit.

Goodness-of-Fit

Chi-Square df Sig.

Pearson 645.276 336 .000

Deviance 285.595 336 .979

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Pseudo R-Square Cox and Snell .354

Nagelkerke .389

McFadden .182

Parameter Estimates

Estimate Std.

Error Wald df Sig. 95% Confidence Interval Lower

Bound

Upper Bound

Threshold

[Y = 1] -32.831 6.555 25.082 1 .000 -45.680 -19.983

[Y = 2] -29.518 6.424 21.112 1 .000 -42.109 -16.927

[Y = 3] -26.914 6.350 17.963 1 .000 -39.360 -14.468

Location

TOT_E .138 .112 1.521 1 .217 -.081 .357

TOT_SOC -.154 .090 2.917 1 .088 -.330 .023

TOT_GE .448 .148 9.127 1 .003 -.738 .157

TOT_GO .460 .134 11.791 1 .001 -.722 .197

TOT_CP .089 .054 2.758 1 .097 -.016 .195

TOT_M .674 .104 42.370 1 .000 -.877 .471

Test of Parallel Lines

Model -2 Log

Likelihood

Chi-Square df Sig.

Null Hypothesis 291.140

General 196.397 94.744 12 .230

The null hypothesis states that the location parameters (slope coefficients) are the same across response categories.^a

a. Link function: Logit.

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1 CHAPTER 1 INTRODUCTION

1.1 Background of the Study

The initial step in realizing a construction project is to understand the nature of the dynamic and complex projects. Construction project is a mission, undertaken to create a unique facility, product or service within the specified scope, quality, time, and cost (Chitkara, 2004). Construction activities are implemented only once and generally occur within a short period of time. The series of activities of a construction project is related to each other and they occur sequentially. It usually begins with the emergence of a necessity, followed by the feasibility study phase, design and planning stage of the procurement and implementation phase, to stage of use. Each stage of the activities has a different timescale and necessary cost estimates. The cost estimate aims to predict the magnitude of the costs incurred to implement an activity in the future. Conceptual cost estimation is one of the most critical tasks in the early stages in the life cycle of a building project (Trost et al., 2003). Fast and accurate estimation of project cost is becoming one of the key factors influencing the agility and competitiveness of enterprises. It also affects most project management activities including project bidding, project planning, risk control, quality and cost management, and resource allocation (Sung et al., 2007).

Cost estimate is a prediction of quantities, cost, or price of resources required by the scope of an asset investment option, activity, or project. As a prediction, an estimate must address risks and uncertainties (AACE, 2007). The cost estimate is performed in line with a series of project activities, beginning with the estimate until detailed estimate at this stage of the procurement and implementation. Each stage has

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a different method of estimation as the estimation detail, beginning with the preparation or grouping level of employment activity or WBS (Work Breakdown Structure). A complex project is made manageable by first breaking it into individual components in a hierarchical structure, known as Work Breakdown Structure (NetMBA, 2010). This is then followed by calculation of the quantity of work (quantity take-off) based on the drawings and specifications. The next step is to perform a job analysis unit price, which consists of the calculation of resources which form the work that covers the cost of wages, the cost of the appropriate level of productivity tools, and the costs of materials, costs of subcontracting, and other costs necessary to support the implementation and the execution of work.

Detailed cost estimates conducted by the contractor or the owner, among others, aim to obtain the amount of the bid price of a project, to control or monitor action at the time of execution, and to know the magnitude of the owner’s estimate as a reference when assessing bids submitted by contractors.

Park (1992) stated that in the bidding price of the work, the contractor aims to to submit a bid with the best price, which has a great opportunity to earn or win jobs and provides the maximum benefit. Cost project of work consists of material costs, equipment costs, and wage costs. The amount of each fee depends on the quantity of materials and level of equipment and labour productivity are used in accordance with the unit quantity of each kind of work area (m2), volume (m3), m ', unit of lump sum and others.

Barcarini (2004) noted that construction projects are notorious for overrunning budgets because of unforeseen factors. Many cases have been documented from around the world concerning increases in cost beyond the estimate.

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In estimating the magnitude of the cost project, the amount of costs to be incurred at the time of construction cannot be known for certain. It is very closely related to the existence of variables that cannot be estimated with certainty, or the existence of uncertainties during the construction phase, so that it leads to variability in the unit price. For example, price fluctuations of materials, equipment and wages will also cause variability in the quantity of work, and create a difference between the quantities of work performed in the field and that stated in the Bill of quantity. All construction projects, regardless of type and size, involve many significant uncertainties and risks through all their construction phases, from the start up to the completion of the project. AACE (2007) defined uncertainty as unknown future events that cannot be predicted quantitatively within useful limits.

Flyvbjerg et al. (2002) found that project costs have been underestimated in approximately 90% of cases, and that the actual costs turned out to be on average 28% more than the estimated costs. There are many uncertain variables during project implementation that dynamically affect the project duration and cost (del Caño & de la Cruz, 2002).

Flyvbjerg et al. (2004) suggested that larger projects experience greater cost overruns on a percentage basis. In anticipation of losses that will occur as a result of variables that cannot be estimated with certainty or those estimated with uncertainty at the time of estimation, a number of costs need to be allocated as the indirect costs charged to the cost of risk; in this case, costs positioned as a contingency cost.

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4 1.2 Research Problem

The problems that arise in this study are to identify the uncertainty variables and to determine their level of influence in estimating project costs. As pointed out by Flyvbjerg et al. (2003), change in project cost, or cost growth, occurs as a result of many related factors, all of which are associated with some form of uncertainties.

This study is done to avoid the following issues:

1. The offer price is too low, which can result in cost overrun or expenditures greater than the estimated value, which would consequently lead to losses, or

2. The offer price is too high, such that it does not successfully help to win the auction.

A key component of a project budget is contingency cost (Baccarini, 2004).

The level of project risk contingency cost in estimates has a major impact on financial outcomes for project owners. If the contingency cost is too high, it might encourage poor cost management, and causes the project to become uneconomical and finally aborted, or to cause the occurrence of lock-up funds that would not be available for other projects (Dey et al., 1996). On the other hand, if the contingency cost allocation is too low, then it may be too rigid and it may set an unrealistic financial environment, resulting in unsatisfactory performance outcomes (Touran, 2003). In some areas of the public sector there is a tendency for the financial authorities to remove contingency provisions in budget submissions, as contingencies are often seen as facts that leave no allowance for anticipating project risk (Yeo, 1990).

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One way to face the problems mentioned is to use the estimated price at the time of the work unit, an estimator must be able to identify and analyse the uncertainty variables that must be calculated in accordance to situations and conditions that occurred during the implementation. This can be calculated based on past project experience, educational background, as well as information held by either of the tender documents, review the site and from other resources.

The gap between actual costs and estimated costs may result in losses and cost overruns on construction projects. This gap can be due to the uncertainty of the variables that occur in construction projects. Therefore, it is necessary to know and identify the variables of uncertainty to minimize the gap.

With respect to the mentioned opinions, further study needs to be done on the uncertainties that occur in construction projects in Indonesia. The uncertainty in construction projects in Indonesia will be more complex, given the condition of the country that has sociocultural diversity, geographical differences, low education levels, unequal living standards, public economics, social and often occurring political upheavals, and the economic crisis that has not yet recovered. These conditions will have a significant influence on the realization of a project as well.

According to Tunardih et al. (2005), in Indonesia wastes in project costs mainly occur due to design changes and additional work required by project owners.

1.3 Research Gap

When determining the amount of the contingency cost at the time of project bidding, contractors often do not specify the uncertainty factors that affect the project cost estimate. Therefore, they need a study to identify and to analyse events that

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cannot be predicted with certainty or that cannot be stated clearly, which is called the uncertainty factors. To avoid losses or cost overrun, the contractor is expected to estimate the cost of the project to the optimum.

Choudhry (2004) defined the cost overruns as the difference between the original cost estimate of project and actual construction cost on completion of works of a commercial sector construction project. Contingency cost allocation should be minimized by making the best estimate. To clarify any vagueness or if there is a lack of information about anything, contractors can ask directly the owner of the project or related parties.

Harbuck (2004) documented three major categories of uncertainty in construction projects as design problems, construction problems, and third party problems. Design problems include design changes, design errors, and ambiguous specifications. Construction problems include differing site conditions, delays, and scope additions. Finally, third party problems include utilities, local government, and permit agencies. Kalayjian (2000) stated that the uncertainty variables in construction projects may arise from ambiguously specified project scope, unclear boundaries of work, inaccurate estimation, and price fluctuations.

Frimpong (2003) added by saying that uncertainty variables in construction projects are affected by improper planning and management experience limitation.

On a different but related perspective, Long et al. (2008) said that the uncertainty variables in construction projects are affected by poor site supervision and management and poor project management assistance. Then, Nega (2008) stated that the uncertainty variables in construction projects are affected by change of weather conditions or subsoil conditions. Huang (2006) concluded that the uncertainty

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variables in construction projects are affected by the project design and construction.

However, Ghosh and Jintanapakanont (2004) identified nine critical risk or uncertainty factors, which include financial and economic factors, contractual and legal factors, subcontractor-related factors, operational factors, safety and social factors, design factor, force majeure factors, physical factors, and delay.

With respect to the mentioned opinions, further study needs to be done on the uncertainties that occur in construction projects in Indonesia, it is aims to improve the current knowledge. Further study needs to know and identify the new uncertainty variables to minimize the gap, in which the uncertainty in construction projects in Indonesia will be more complex, given the condition of the country that has sociocultural diversity, geographical differences, low education levels, unequal living standards, public economics, social and often occurring political upheavals, and the economic crisis that has not yet recovered. These conditions will have a significant influence on the realization of a project as well, 12 new uncertainty variables were found affecting construction project

1.4 Research Questions (RQs)

The research questions (RQs) in this study are as follows:

RQ1: What are the uncertainty variables that influence the estimation of project costs?

RQ2: What are the key uncertainty variables that influence the estimation the costs of a project?

RQ3: How the uncertainty variables influence the variable of contingency cost?

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8 1.5 Research Objectives (ROs)

In estimating the magnitude of project costs, the amount of costs to be incurred cannot be known for certain at the time of construction. This is because project costs are closely related to the presence of variables that cannot be estimated with certainty or the presence of uncertainties during the various stages of construction.

Therefore, the aims of this research are as follows:

RO1: To identify of uncertainty variables that influence the estimation project cost.

RO2: To identify the key uncertainty variables that has greater influence in estimating the costs of project work.

RO3: To investigate the impact of uncertainty variables on contingency cost.

1.6 Significance of the Research

As has been mentioned, the costs of a project work to be incurred cannot be known for certain at the time of construction. This is because project costs are closely related to the presence of variables that cannot be estimated with certainty at this stage or the presence of uncertainties during construction. Peeters and Madauss (2008) stated that inaccurate estimation of original or initial cost of a project are due to the technical problems on how to estimate project costs and insufficient project information in the early stages of a project.

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To anticipate the losses that would occur as a result of variables that cannot be estimated with certainty or as a result of uncertainty that could occur, at the time of estimation, a number of costs need to be allocated. These are indirect costs charged as the cost of risk. In this case, the costs are positioned as a contingency cost. Sonmez et al. (2007) studied the financial impact of various uncertainty variables affecting contingency costs during the bidding stages of international construction projects. The cost performance of construction projects is a key success criterion for project sponsors. The cost performance can be measured by comparing project budgets against the final cost of the project. Therefore, the estimation of contingency costs is an important issue in construction projects and this topic is worthy of serious research (Baccarini, 2004).

To solve the aforementioned problems, when estimating the cost of the project work, an estimator must be able to identify and analyse the uncertainty variables that must be calculated in accordance to situations and conditions that would occur during the implementation of the project. Del Cana and De la Cruz (2002) pointed out that during project implementation, many uncertainty variables can dynamically affect the project duration and this would increase the costs of the project.

Research related to uncertainty analysis of the variables affecting construction projects can provide a great benefit to interested parties such as

(a) contractors who need to allocate more optimal contingency costs in accordance with the uncertainty of the most influential variables so as to calculate the best offer price.

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(b) project owners, for them to understand the most influential uncertainty variable, so that they can develop a more realistic estimate, closer to the price offered by the contractor.

1.7 Scope of the Research

The scope of the discussion on the variables of uncertainty in the estimation of cost project work on construction projects is limited by the following points.

(a) This research done only on construction projects in the Province of North Sumatera, especially in the city of Medan. The reason for choosing the city as the location of the study is because the city has many large construction companies and many large projects are implemented by contractors in this city.

(b) In this study, respondents were determined by the qualification of contractors. The respondents were as follows: grade 5 (medium) contractors consisting of 203 respondents, grade 6 (large) contractors consisting of 30 respondents, and grade 7 (large) contractors consisting of 11 respondents. Reasons for choosing these contractors as respondents are that the contractors have a lot of experience and education in the construction field and they already have a pattern of complete and regular management. According to Department of Public Works (2001), the qualifications of contractor companies handling construction projects in Indonesia consist of grade 1, grade 2, grade 3, grade 4, grade 5, grade 6, and grade 7. Table 1.1 shows the level of qualification of contractors in Medan.

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11 Table 1.1

Qualification of Construction Services Business

Qualifications of the construction (Contractor)

Qualification Classes Net worth

(Rupiah)

The limit value of the project work (Rupiah)

Grade 7 Large > 10 Billion > 2.5 Billion- unlimited

Grade 6 Large > 3 Billion > 2.5 Billion- 100 Billion

Grade 5 Medium > 2.5 Billion > 2.5 Billion- 50 Billion

Grade 4 Small > 400 Million ≤ 2.5 Billion

Grade 3 Small > 100 Million ≤ 1.75 Billion

Grade 2 Small > 50 Million ≤ 1 Billion

Grade 1 Micro (Business Agency) - ≤ 300 Million

Grade 1 Micro (Individual) - ≤ 100 Million

Source. Department of Public Works (2014)

1.8 Definition of Key Terms

The terms used in this research have been defined differently by various researchers and practitioners depending on their individual contexts. The definitions of the terms used for the purpose of this study are therefore identified below:

Table. 1.2

Definitions of The Terms

Name Definitions Ref

Cost The cash or the value of cash equivalent that is sacrificed for goods or services that are expected to provide benefits currently or in the future for the organization

(Hansen, 2005)

Cost A key consideration throughout the project management life cycle and can be considered as one of the most important parameters of a project and the driving force a behind the success of the project

(Azhar et al., 2008).

Cost Wealth, in the form of cash or noncash, sacrificed for goods and services which are expected to provide current or future benefits for the organization

(Hansen et al., 2009).

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12

Estimates Used primarily as inputs for budgeting cost in value analysis, in decision making in business, in asset and project planning, or in project cost, and in scheduling control processes.

(AACE, 2007)

Estimation Estimation is conducted to assess the feasibility of a project to be implemented or to make a selection from several alternative designs.

(Ahuja, 1994)

Cost Estimation Cost estimation is one of the most critical tasks in the early stages in the life cycle of a building project

(Trost et al., 2003).

Uncertainty As unknown future events that cannot be predicted quantitatively within useful limits.

AACE (2007)

Uncertainties Can cause losses that can lead to increased costs, time delays, and reduced project quality.

(Simu, 2006)

External uncertainty factors

Factors that are outside the project environment and influence the project activities.

(Yeo, 1990)

Internal uncertainty factors Factors of uncertainty that arise from within the project environment.

(Yeo, 1990)

Contingency Directly related to the accuracy of base estimates because it is included in the cost estimate, which is prepared before the start of project execution.

(Molenaar, 2005)

Contingency The amount of funds available in reserve to face the uncertainties related to construction projects.

(Mak and Picken 2000)

Contingency As an amount added to an estimate to allow for items, conditions, or events for which the state, occurrence, or effect are uncertain and that experience have shown would most likely result, in aggregate, in additional costs.

(AACE,2007)

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13

Direct costs All costs that become a permanent component of the final results of the project which consists of the following costs

(AACE, 2007)

Indirect Cost Indirect costs are costs that support the work, but not listed as the nature of the current job payment.

(Ahuja. 1994)

Construction project A mission, undertaken to create a unique facility, product or service within the specified scope, quality, time, and cost.

(Chitkara, 2004).

Cost overruns Difference between the original cost estimate of project and actual construction cost on completion of works of a commercial sector construction project.

Choudhry (2004)

1.9 Organisation of the Thesis

The thesis is divided into six chapters. Chapter 1 provides an introduction to this study, background research, research problem, research questions, research objectives, significance of the research, scope of the research, and the organization of the thesis. Chapter 2 focuses on a review of existing literature in relation to the research topic. The first section reviews the history and evolution of the estimated cost of construction projects, especially about the variables related to the uncertainty in estimating the costs of construction projects. This is then followed by the identification of the uncertainty variables that occur in construction projects. Chapter 3 describes the research methodology used in this study. This chapter is divided into sections that discuss the sampling frame, the sample size, and the research instruments. This chapter was developed based on the literature review provided in Chapter 2. Chapter 4 explains the data collection methods and the validity and

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reliability of measurement and statistical analysis. Chapter 5 focuses on the findings and discussion. Finally, Chapter 6 provides conclusion and recommendations of the study.

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59 Table 2.1

The Uncertainties Variable That Influence Project Cost Estimate Based on Other Studies

No. Uncertainty Variables

Authors/ Years

Yeo (1990) Kalayjian (2000) Han et al (2001) Chimwaso (2001) Pakkala (2002) Chan et al (2003) Frimpong (2003) Ghosh et al (2004) Thevendran et al (2004) Harbuck (2004) Long et al (2004) Ren et al (2004) Bing et al (2005) Chan et al (2005) Azhar et al (2008) Broadbent et al (2008) Long et al (2008) Nega (2008) Creedy et.al (2010) Memon et al (2011) Subramanyan et al (2012) Doloi (2013) Frequency

1 The inflation rate x x 2

2 Exchange rate x x 2

3 Social and cultural conditions x x x x 4

4 Work ethics and religious beliefs. ^x ¹

5 The physical condition x x x x x x x x 8 6 Interpretation and implementation of government policy on construction sector ^x ^x ^x ³

7 Scale or scope of project x x x x x x 6

8 The project site x x x x x x x 7

9 The implementation period x x 2

10 Managerial ability of the team involved ^x ^x ^x ^x ^x ^x ⁶

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60 Table 2.1 continued.

No. Uncertainty Variables

Authors/ Years

Yeo (1990) Kalayjian (2000) Han et al (2001) Chimwaso (2001) Pakkala (2002) Chan et al (2003) Frimpong (2003) Ghosh et al (2004) Thevendran et al (2004) Harbuck (2004) Long et al (2004) Ren et al (2004) Bing et al (2005) Chan et al (2005) Azhar et al (2008) Broadbent et al (2008) Long et al (2008) Nega (2008) Creedy et.al (2010) Memon et al (2011) Subramanyan et al (2012) Doloi (2013) Frequency

11

Availability and the working relationship between the Contractor with the supplier

x x x 3

12 Technical problem x x x 3

13 Price fluctuations x x x x x x 6

14 Design changes ^x ^x ^x ^x ^x ^x ^x ^x ^x ^x ¹⁰

15 Inadequate planning x x x x x x 6

16 Experience limitation. x x x x x 5

17 Financial and economic x x x x x x x x 8

18 Contractual and legal x x x x x x 6

19 Operational ^x ^x ²

20 Delay x x x x x x 6

21 Human risk x x x x x 5

22 Local government ^x ¹

23 Permit agencies ^x ¹

24 Wrong methods ^x ^x ^x ^x ⁴

25 Political x 1

26 Additional work x x x 3

27 Insufficient data collection and

survey before design ^x ^x ²

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61 2.4 Research Framework

The conceptual framework is a framework that illustrates the relationship between the variables in this study (Sekaran, 2003). The development of the conceptual framework is done after reviews of literature on uncertainty variable theories and concept, philosophies and empirical researches.

Base on the discussion in previous chapters, six independent variables that represent the uncertainty variables concept has been identified, selected and operationalized in this study (see Figure 2.1). These variables are economics, social and cultural, geography, government policy, complexity of the project, and project management handling. Meanwhile, the dependent variable in this study is contingency cost.